




import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_fnet import FNetTokenizer
else:
						SCREAMING_SNAKE_CASE:     Dict									= None

SCREAMING_SNAKE_CASE:     int									= logging.get_logger(__name__)
SCREAMING_SNAKE_CASE:     Union[str, Any]									= {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

SCREAMING_SNAKE_CASE:     str									= {
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

SCREAMING_SNAKE_CASE:     List[Any]									= {
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

SCREAMING_SNAKE_CASE:     str									= '''▁'''



class    lowercase_		(__A ):
						lowerCAmelCase__			      =VOCAB_FILES_NAMES
						lowerCAmelCase__			      =PRETRAINED_VOCAB_FILES_MAP
						lowerCAmelCase__			      =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						lowerCAmelCase__			      =['''input_ids''', '''token_type_ids''']
						lowerCAmelCase__			      =FNetTokenizer



						def __init__(			self	:   Union[str, Any]					,							snake_case__	:   int=None					,							snake_case__	:   Any=None					,							snake_case__	:   Dict=False					,							snake_case__	:   Tuple=True					,							snake_case__	:   Union[str, Any]=True					,							snake_case__	:   Dict="<unk>"					,							snake_case__	:   Optional[Any]="[SEP]"					,							snake_case__	:   Any="<pad>"					,							snake_case__	:   List[Any]="[CLS]"					,							snake_case__	:   str="[MASK]"					,							**snake_case__	:   Union[str, Any]					,							):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							(
								    AddedToken(UpperCamelCase__					,							lstrip=UpperCamelCase__					,							rstrip=UpperCamelCase__					,							normalized=UpperCamelCase__  )
								    if isinstance(UpperCamelCase__					,							UpperCamelCase__  )
								    else mask_token
								)

								super().__init__(
								    UpperCamelCase__					,							tokenizer_file=UpperCamelCase__					,							do_lower_case=UpperCamelCase__					,							remove_space=UpperCamelCase__					,							keep_accents=UpperCamelCase__					,							unk_token=UpperCamelCase__					,							sep_token=UpperCamelCase__					,							pad_token=UpperCamelCase__					,							cls_token=UpperCamelCase__					,							mask_token=UpperCamelCase__					,							**UpperCamelCase__					,							)

								SCREAMING_SNAKE_CASE_					      =							do_lower_case
								SCREAMING_SNAKE_CASE_					      =							remove_space
								SCREAMING_SNAKE_CASE_					      =							keep_accents
								SCREAMING_SNAKE_CASE_					      =							vocab_file
								SCREAMING_SNAKE_CASE_					      =							False if not self.vocab_file else True



						def 	__a					(			self	:   Any					,							snake_case__	:   Dict					,							snake_case__	:   str = None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[self.sep_token_id]
								SCREAMING_SNAKE_CASE_					      =							[self.cls_token_id]
								if token_ids_a is None:
										return cls + token_ids_a + sep
								return cls + token_ids_a + sep + token_ids_a + sep



						def 	__a					(			self	:   Optional[int]					,							snake_case__	:   Optional[Any]					,							snake_case__	:   Union[str, Any] = None  ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_					      =							[self.sep_token_id]
								SCREAMING_SNAKE_CASE_					      =							[self.cls_token_id]

								if token_ids_a is None:
										return len(cls + token_ids_a + sep  ) * [0]
								return len(cls + token_ids_a + sep  ) * [0] + len(token_ids_a + sep  ) * [1]






						def 	__a					(			self	:   Tuple					,							snake_case__	:   List[str]					,							snake_case__	:   List[str] = None  ):

								"""simple docstring"""

								if not os.path.isdir(UpperCamelCase__  ):
										logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
										return
								SCREAMING_SNAKE_CASE_					      =							os.path.join(
								    UpperCamelCase__					,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

								if os.path.abspath(self.vocab_file  ) != os.path.abspath(UpperCamelCase__  ):
										copyfile(self.vocab_file					,							UpperCamelCase__  )

								return (out_vocab_file,)







'''simple docstring'''







import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : List[Any]      			=     ['''image_processor''', '''tokenizer''']
  _a  : List[Any]      			=     '''ViTImageProcessor'''
  _a  : int      			=     ('''CLIPTokenizer''', '''CLIPTokenizerFast''')







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			None
         if "feature_extractor" in kwargs:
                warnings.warn(
                    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
                    ' instead.'   ,	UpperCamelCase__   ,	)
                a_						     =			kwargs.pop('feature_extractor'	)

         a_						     =			image_processor if image_processor is not None else feature_extractor
         if image_processor is None:
                raise ValueError('You need to specify an `image_processor`.'	)
         if tokenizer is None:
                raise ValueError('You need to specify a `tokenizer`.'	)

         super().__init__(UpperCamelCase__   ,	UpperCamelCase__	)







  def __call__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	**UpperCamelCase__	):






         """simple docstring"""


         if text is None and visual_prompt is None and images is None:
                raise ValueError('You have to specify either text, visual prompt or images.'	)

         if text is not None and visual_prompt is not None:
                raise ValueError('You have to specify exactly one type of prompt. Either text or visual prompt.'	)

         if text is not None:
                a_						     =			self.tokenizer(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if images is not None:
                a_						     =			self.image_processor(UpperCamelCase__   ,	return_tensors=UpperCamelCase__   ,	**UpperCamelCase__	)

         if visual_prompt is not None and images is not None:
                a_						     =			{
                    'pixel_values': image_features.pixel_values,
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         elif text is not None and images is not None:
                a_						     =			image_features.pixel_values
                return encoding
         elif text is not None:
                return encoding
         elif visual_prompt is not None:
                a_						     =			{
                    'conditional_pixel_values': prompt_features.pixel_values,
                }
                return encoding
         else:
                return BatchEncoding(data=dict(**UpperCamelCase__	)   ,	tensor_type=UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.batch_decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         return self.tokenizer.decode(*UpperCamelCase__   ,	**UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor_class







  @property
  def    _a       (			self	):






         """simple docstring"""


         warnings.warn(
             '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,	UpperCamelCase__   ,	)
         return self.image_processor




